國立高雄大學圖資館 |

Language: English

Back

Algorithms and programs of dynamic m...

Nagy, Ivan.

Algorithms and programs of dynamic mixture estimationunified approach to different types of components /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Algorithms and programs of dynamic mixture estimationby Ivan Nagy, Evgenia Suzdaleva.
Reminder of title:	unified approach to different types of components /
Author:	Nagy, Ivan.
other author:	Suzdaleva, Evgenia.
Published:	Cham :Springer International Publishing :2017.
Description:	ix, 113 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Estimation theory.
Online resource:	http://dx.doi.org/10.1007/978-3-319-64671-8
ISBN:	9783319646718$q(electronic bk.)

Algorithms and programs of dynamic mixture estimationunified approach to different types of components /
Nagy, Ivan.

Algorithms and programs of dynamic mixture estimationunified approach to different types of components /[electronic resource] :by Ivan Nagy, Evgenia Suzdaleva. - Cham :Springer International Publishing :2017. - ix, 113 p. :ill., digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

Introduction -- Basic Models -- Statistical Analysis of Dynamic Mixtures -- Dynamic Mixture Estimation -- Program Codes -- Experiments -- Appendices.

This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a scheme for constructing the estimation algorithm for a mixture of components modeled by distributions with reproducible statistics. It offers the recursive estimation of dynamic mixtures, which are free of iterative processes and close to analytical solutions as much as possible. In addition, these methods can be used online and simultaneously perform learning, which improves their efficiency during estimation. The book includes detailed program codes for solving the presented theoretical tasks. Codes are implemented in the open source platform for engineering computations. The program codes given serve to illustrate the theory and demonstrate the work of the included algorithms.

ISBN: 9783319646718$q(electronic bk.)

Standard No.: 10.1007/978-3-319-64671-8doiSubjects--Topical Terms:

181864
Estimation theory.

LC Class. No.: QA276.8 / .N34 2017

Dewey Class. No.: 519.544

Algorithms and programs of dynamic mixture estimationunified approach to different types of components /
LDR:02212nmm a2200337 a 4500 001 521364
003 DE-He213
005 20180319162209.0
006 m d
007 cr nn 008maaau
008 180504s2017 gw s 0 eng d
020 $a 9783319646718$q(electronic bk.)
020 $a 9783319646701$q(paper)
024 7 $a 10.1007/978-3-319-64671-8 $2 doi
035 $a 978-3-319-64671-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276.8 $b .N34 2017
072 7 $a PBT $2 bicssc
072 7 $a PBWL $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.544 $2 23
090 $a QA276.8 $b .N152 2017
100 1 $a Nagy, Ivan. $3 791362
245 1 0 $a Algorithms and programs of dynamic mixture estimation $h [electronic resource] : $b unified approach to different types of components / $c by Ivan Nagy, Evgenia Suzdaleva.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a ix, 113 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in statistics, $x 2191-544X
505 0 $a Introduction -- Basic Models -- Statistical Analysis of Dynamic Mixtures -- Dynamic Mixture Estimation -- Program Codes -- Experiments -- Appendices.
520 $a This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a scheme for constructing the estimation algorithm for a mixture of components modeled by distributions with reproducible statistics. It offers the recursive estimation of dynamic mixtures, which are free of iterative processes and close to analytical solutions as much as possible. In addition, these methods can be used online and simultaneously perform learning, which improves their efficiency during estimation. The book includes detailed program codes for solving the presented theoretical tasks. Codes are implemented in the open source platform for engineering computations. The program codes given serve to illustrate the theory and demonstrate the work of the included algorithms.
650 0 $a Estimation theory. $3 181864
650 0 $a Regression analysis $x Mathematical models. $3 405172
650 1 4 $a Mathematics. $3 184409
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
650 2 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Systems Theory, Control. $3 274654
650 2 4 $a Simulation and Modeling. $3 273719
650 2 4 $a Algorithms. $3 184661
700 1 $a Suzdaleva, Evgenia. $3 791363
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in statistics. $3 557771
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-64671-8
950 $a Mathematics and Statistics (Springer-11649)